Examining avian influenza virus exposure in seabirds of the northwest Atlantic in 2022 and 2023 via antibodies in eggs
Bibliographic record
Abstract
Abstract Seabirds are frequently infected by avian influenza virus (AIV), which prior to 2021 primarily consisted of low-pathogenic AIV with limited reports of disease during infection. However, since highly pathogenic AIV (HPAIV) H5N1 clade 2.3.4.4b was introduced to North America in late 2021, HPAIV outbreaks in seabirds have occurred in multiple regions, with high levels of morbidity and mortality in many species. While monitoring active viral infections is critical for tracking disease burden, monitoring prior viral exposure via antibody detection in species that experienced large outbreaks is important for identifying individual- and population-level impacts of AIV on immunity and survival. We capitalized on ongoing egg collection programmes to assess the prevalence of antibodies against AIV nucleoprotein (NP) and hemagglutinin subtype 5 (H5) in 523 eggs collected in 2022 and 2023 from 11 seabird species that breed in the northwestern Atlantic, including primarily samples from eastern Canada and two from western Iceland. The prevalence of AIV antibodies in eggs varied across regions, species and years. American common eider (Somateria mollissima dresseri) eggs had the highest AIV antibody prevalence compared to sympatric species in 2023. Longitudinal samples were available for northern gannets (Morus bassanus) and American herring gulls (Larus argentatus smithsoniansus) at several sites, where the prevalence of anti-NP and anti-H5 antibodies increased from 2022 to 2023. Examining AIV antibody prevalence in seabird eggs can be a useful tool to investigate population-level AIV exposure, while we acknowledge our limited understanding of differential antibody waning rates and the relationship between titre and susceptibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".